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  3. A/B Testing in Python

Exercise

A/B test data cleaning

A/B testing data cleaning is no different from the usual process taught in data cleaning and manipulation courses. However, understanding the context and data logging behavior of each A/B test on a case-by-case basis enables you to make decisions about how to handle messy data. Dropping or keeping duplicates or missing values are two cases where we need to be careful with our metric definitions and how consistent these are across variants.

AdSmart and homepage DataFrames as well as pandas and numpy libraries are loaded for you.

The Adsmart Kaggle dataset source is linked here.

Instructions 1/3

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  • Check for duplicate rows in the AdSmart dataset by printing number of rows.